Stock market decision support modeling with tree-based AdaBoost ensemble machine learning models

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass expressed as compressed sward height (CSH) from readily accessible meteorological, optical (Sentinel-2) and radar satellite data (Sentinel-1). This study assumed that combining heterogeneous data sources, data transformations and machine learning methods would improve the robustness and the accuracy of the developed models. A total of 72,795 records of CSH with a spatial positioning, collected in 2018 and 2019, were used and aggregated according to a pixel-like pattern. The resulting dataset was split into a training one with 11,625 pixellated records and an independent validation one with 4952 pixellated records. The models were trained with a 19-fold cross-validation. A wide range of performances was observed (with mean root mean square error (RMSE) of cross-validation ranging from 22.84 mm of CSH to infinite-like values), and the four best-performing models were a cubist, a glmnet, a neural network and a random forest. These models had an RMSE of independent validation lower than 20 mm of CSH at the pixel-level. To simulate the behavior of the model in a decision support system, performances at the paddock level were also studied. These were computed according to two scenarios: either the predictions were made at a sub-parcel level and then aggregated, or the data were aggregated at the parcel level and the predictions were made for these aggregated data. The results obtained in this study were more accurate than those found in the literature concerning pasture budgeting and grassland biomass evaluation. The training of the 124 models resulting from the described framework was part of the realization of a decision support system to help farmers in their daily decision making.

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Ensemble machine learning models for aviation incident risk prediction

Decision Support Systems ◽

10.1016/j.dss.2018.10.009 ◽

2019 ◽

Vol 116 ◽

pp. 48-63 ◽

Cited By ~ 21

Author(s):

Xiaoge Zhang ◽

Sankaran Mahadevan

Keyword(s):

Machine Learning ◽

Risk Prediction ◽

Learning Models ◽

Ensemble Machine Learning ◽

Machine Learning Models

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Data-Driven Approach for Predicting and Explaining the Risk of Long-Term Unemployment

E3S Web of Conferences ◽

10.1051/e3sconf/202021401023 ◽

2020 ◽

Vol 214 ◽

pp. 01023

Author(s):

Linan (Frank) Zhao

Keyword(s):

Machine Learning ◽

Age Groups ◽

Learning Models ◽

Public Authorities ◽

Ensemble Machine Learning ◽

European Public ◽

Data Driven Approach ◽

Using Data ◽

Machine Learning Models

Long-term unemployment has significant societal impact and is of particular concerns for policymakers with regard to economic growth and public finances. This paper constructs advanced ensemble machine learning models to predict citizens’ risks of becoming long-term unemployed using data collected from European public authorities for employment service. The proposed model achieves 81.2% accuracy on identifying citizens with high risks of long-term unemployment. This paper also examines how to dissect black-box machine learning models by offering explanations at both a local and global level using SHAP, a state-of-the-art model-agnostic approach to explain factors that contribute to long-term unemployment. Lastly, this paper addresses an under-explored question when applying machine learning in the public domain, that is, the inherent bias in model predictions. The results show that popular models such as gradient boosted trees may produce unfair predictions against senior age groups and immigrants. Overall, this paper sheds light on the recent increasing shift for governments to adopt machine learning models to profile and prioritize employment resources to reduce the detrimental effects of long-term unemployment and improve public welfare.

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Profitable Ranking of Stock Market Organizations Using Different Machine Learning Models

10.1109/r10-htc53172.2021.9641677 ◽

2021 ◽

Author(s):

Muhaddid Alavi ◽

Selina Sharmin ◽

Ashraf Uddin ◽

Tanvir Ahammad ◽

Fatema Siddika

Keyword(s):

Machine Learning ◽

Stock Market ◽

Learning Models ◽

Machine Learning Models

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Efficient Breast Cancer Prediction Using Ensemble Machine Learning Models

2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) ◽

10.1109/rteict46194.2019.9016968 ◽

2019 ◽

Cited By ~ 1

Author(s):

Naveen ◽

R. K. Sharma ◽

Anil Ramachandran Nair

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Learning Models ◽

Cancer Prediction ◽

Ensemble Machine Learning ◽

Machine Learning Models

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EFFECTIVE COMMUNICATION OF MACHINE LEARNING MODELS IN CLINICAL DECISION SUPPORT TOOLS FOR PAH RISK PREDICTION

CHEST Journal ◽

10.1016/j.chest.2021.07.1277 ◽

2021 ◽

Vol 160 (4) ◽

pp. A1396-A1397

Author(s):

Raymond Benza ◽

Manreet Kanwar ◽

James Antaki ◽

Aditi Dhabalia ◽

Mia Manavalan ◽

...

Keyword(s):

Machine Learning ◽

Decision Support ◽

Risk Prediction ◽

Clinical Decision Support ◽

Clinical Decision ◽

Decision Support Tools ◽

Learning Models ◽

Support Tools ◽

Clinical Decision Support Tools ◽

Machine Learning Models

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Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication–Related Clinical Decision Support System: Model Development and Validation (Preprint)

10.2196/preprints.19489 ◽

2020 ◽

Author(s):

Tahmina Nasrin Poly ◽

Md.Mohaimenul Islam ◽

Muhammad Solihuddin Muhtar ◽

Hsuan-Chia Yang ◽

Phung Anh (Alex) Nguyen ◽

...

Keyword(s):

Machine Learning ◽

Decision Support ◽

Clinical Decision Support ◽

Prediction Models ◽

Clinical Decision ◽

Gradient Boosting ◽

Support Vector ◽

Learning Models ◽

Alert Fatigue ◽

Machine Learning Models

BACKGROUND Computerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies reported that approximately 90%-96% of alerts are overridden by physicians, which raises questions about the effectiveness of CDSSs. There is intense interest in developing sophisticated methods to combat alert fatigue, but there is no consensus on the optimal approaches so far. OBJECTIVE Our objective was to develop machine learning prediction models to predict physicians’ responses in order to reduce alert fatigue from disease medication–related CDSSs. METHODS We collected data from a disease medication–related CDSS from a university teaching hospital in Taiwan. We considered prescriptions that triggered alerts in the CDSS between August 2018 and May 2019. Machine learning models, such as artificial neural network (ANN), random forest (RF), naïve Bayes (NB), gradient boosting (GB), and support vector machine (SVM), were used to develop prediction models. The data were randomly split into training (80%) and testing (20%) datasets. RESULTS A total of 6453 prescriptions were used in our model. The ANN machine learning prediction model demonstrated excellent discrimination (area under the receiver operating characteristic curve [AUROC] 0.94; accuracy 0.85), whereas the RF, NB, GB, and SVM models had AUROCs of 0.93, 0.91, 0.91, and 0.80, respectively. The sensitivity and specificity of the ANN model were 0.87 and 0.83, respectively. CONCLUSIONS In this study, ANN showed substantially better performance in predicting individual physician responses to an alert from a disease medication–related CDSS, as compared to the other models. To our knowledge, this is the first study to use machine learning models to predict physician responses to alerts; furthermore, it can help to develop sophisticated CDSSs in real-world clinical settings.

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